Precipitation nowcasting is very important for the sectors which critically depend on timely and accurate weather information. One of the challenges of precipitation nowcasting is radar echo extrapolation which predicts the radar echo images accurately. Nowadays, the methods of radar echo extrapolation are mostly based on ConvRNNs. Unfortunately, as lead time increases, these methods unavoidably suffer from the problem that high reflectivity values are underestimated. Therefore, we propose a forecast-refinement neural network based on DyConvGRU and U-Net to improve the predicting ability for high reflectivity during radar echo extrapolation. To improve the model’s ability to predict high reflectivities, dynamic convolution, and the forecast-refinement architecture are applied. And to obtain more realistic results, the WGAN’s training strategy is adopted to train the forecast module and the refinement module. Through experiments on a radar dataset from Shanghai, China, the results show that our proposed method obtains higher Probability of Detection (POD), Critical Success Index (CSI), Heidke Skill Score (HSS), and lower False Alarm Rate(FAR).

In this study, a deep learning method called Lightning-SN was developed and used for cloud-to-ground (CG) lightning identification. Based on artificial scenarios, this network model selects radar products that exhibit characteristic factors closely related to lightning. Advanced time of arrival and direction lightning positioning data were used as the labeling factors. The Lightning-SN model was constructed based on an encoder–decoder structure with 25 convolutional layers, five pooling layers, five upsampling layers, and a sigmoid activation function layer. Additionally, the maximum pooling index method was adopted in Lightning-SN to avoid characteristic boundary information loss in the pooling process. The gradient harmonizing mechanism was used as the loss function to improve the model performance. The evaluation results showed that the Lightning-SN improved the segmentation accuracy of the CG lightning location compared with the traditional threshold method, according to the 6-minute operating period of the current S-band Doppler radar, exhibiting a better performance in terms of lightning location identification based on high-resolution radar data. The model was applied to the Ningbo area of Zhejiang Province, China. It was applied to the lightning hazard prevention in the hazardous chemical park in Ningbo. The composite reflectivity and radial velocity were the two dominant factors, with a greater influence on the model performance than other factors.